首页> 外文期刊>International journal of heritage in the digital era >Acoustic Data Analysis from Multi-Sensor Capture in Rare Singing: Cantu in Paghjella Case Study
【24h】

Acoustic Data Analysis from Multi-Sensor Capture in Rare Singing: Cantu in Paghjella Case Study

机译:稀有唱歌中多传感器捕获的声学数据分析:Paghjella中的Cantu案例研究

获取原文
获取原文并翻译 | 示例
           

摘要

This paper deals with new capturing technologies to safeguard and transmit endangered intangible cultural heritage including Corsican multipart singing technique. The described work, part of the European FP7 i-Treasures project, aims at increasing our knowledge on rare singing techniques. This paper includes (ⅰ) a presentation of our light hyper-helmet with 5 non-invasive sensors (microphone, camera, ultrasound sensor, piezoelectric sensor, electroglottograph), (ⅱ) the data acquisition process and software modules for visualization and data analysis, (ⅲ) a case study on acoustic analysis of voice quality for the UNESCO labelled traditional Cantu in Poghjello. We have identified specific features for this singing style, such as changes in vocal quality, especially concerning the energy in the speaking and singing formant frequency region, a nasal vibration that seems to occur during singing, as well as laryngeal mechanism characteristics. These capturing and analysis technologies will contribute to define relevant features for a future educational platform.
机译:本文探讨了新的捕获技术,以保护和传播濒危的非物质文化遗产,包括科西嘉人的多声演唱技术。所描述的工作是欧洲FP7 i-Treasures项目的一部分,旨在增加我们对稀有歌唱技巧的了解。本文包括(ⅰ)介绍我们的轻型超头盔,其中包括5种非侵入式传感器(麦克风,照相机,超声传感器,压电传感器,电描记器);(ⅱ)数据采集过程和用于可视化和数据分析的软件模块, (ⅲ)在Poghjello被联合国教科文组织标记为传统Cantu的语音质量声学分析案例研究。我们已经确定了这种歌唱风格的特定特征,例如声音质量的变化,特别是关于口语和歌唱共振峰频率区域中的能量,唱歌过程中似乎发生的鼻腔振动以及喉部机制特征。这些捕获和分析技术将有助于定义未来教育平台的相关功能。

著录项

  • 来源
  • 作者单位

    Phonetics and Phonology Laboratory, LPP-CNRS, UMR7018, Univ. Paris3 Sorbonne Nouvelle;

    Phonetics and Phonology Laboratory, LPP-CNRS, UMR7018, Univ. Paris3 Sorbonne Nouvelle;

    Universite Pierre Marie Curie, Paris, France,Signal Processing and Machine Learning Lab, ESPCI Paris-Tech, Paris, France;

    Phonetics and Phonology Laboratory, LPP-CNRS, UMR7018, Univ. Paris3 Sorbonne Nouvelle;

    Phonetics and Phonology Laboratory, LPP-CNRS, UMR7018, Univ. Paris3 Sorbonne Nouvelle;

    Phonetics and Phonology Laboratory, LPP-CNRS, UMR7018, Univ. Paris3 Sorbonne Nouvelle;

    Universite Pierre Marie Curie, Paris, France,Signal Processing and Machine Learning Lab, ESPCI Paris-Tech, Paris, France;

    Phonetics and Phonology Laboratory, LPP-CNRS, UMR7018, Univ. Paris3 Sorbonne Nouvelle;

    Universite Pierre Marie Curie, Paris, France,Signal Processing and Machine Learning Lab, ESPCI Paris-Tech, Paris, France;

    Signal Processing and Machine Learning Lab, ESPCI Paris-Tech, Paris, France;

    Vocal Tract Visualization Lab, Univ of Maryland Dental School, Baltimore, USA;

    Phonetics and Phonology Laboratory, LPP-CNRS, UMR7018, Univ. Paris3 Sorbonne Nouvelle;

    Universite Pierre Marie Curie, Paris, France,Signal Processing and Machine Learning Lab, ESPCI Paris-Tech, Paris, France;

    Phonetics and Phonology Laboratory, LPP-CNRS, UMR7018, Univ. Paris3 Sorbonne Nouvelle;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号